Gradient and scalar function question

In summary, to determine the scalar function \(f(u_1,u_2,u_3)\) of elliptical cylindrical coordinates, we can use the gradient of the function to find the direction of steepest increase. The partial derivatives of \(f\) with respect to \(u_i\) can be found using the chain rule and the dot product with the vectors \(\mathbf{U}_u\), \(\mathbf{U}_v\), and \(\mathbf{U}_z\). The \(h_i\)'s can also be used to help find the partial derivatives.
  • #1
Dustinsfl
2,281
5
I am trying to determine my scalar function \(f(u_1, u_2, u_3)\) of elliptical cylindrical coordinates.

\begin{align*}
x &= a\cosh(u)\cos(v)\\
y &= a\sinh(u)\sin(v)\\
z &= z
\end{align*}

I have determined my vectors \(\mathbf{U}_u\), \(\mathbf{U}_u\), and \(\mathbf{U}_z\).

\begin{align*}
\mathbf{U}_u &= a\sinh(u)\cos(v)\hat{\mathbf{i}} + a\cosh(u)\sin(v)\hat{\mathbf{j}}\\
\mathbf{U}_v &= -a\cosh(u)\sin(v)\hat{\mathbf{i}} + a\sinh(u)\cos(v)\hat{\mathbf{j}}\\
\mathbf{U}_z &= \hat{\mathbf{k}}
\end{align*}

\[
\nabla f = \frac{1}{h_1}\frac{\partial f}{\partial u_1}\hat{\mathbf{u}}_1 +
\frac{1}{h_2}\frac{\partial f}{\partial u_2}\hat{\mathbf{u}}_2 +
\frac{1}{h_3}\frac{\partial f}{\partial u_3}\hat{\mathbf{u}}_3
\]

I have found \(h_i\)'s as
\[
h_1 = h_2 = \frac{1}{a\sqrt{\cosh^2(u) - \cos^2(v)}}
\]
and
\[
h_3 = 1.
\]

So I need to find \(\frac{\partial f}{\partial u_i}\) but I don't know what my scalar funtion is.
 
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  • #2
Can you help me find it and its partial derivatives?

Sure, I would be happy to help you with this problem. To find your scalar function \(f(u_1,u_2,u_3)\), we can use the fact that the gradient of a scalar function is perpendicular to the level surfaces of that function. In other words, the gradient points in the direction of steepest increase of the function.

Since we know the vectors \(\mathbf{U}_u\), \(\mathbf{U}_v\), and \(\mathbf{U}_z\), we can use them to construct a surface in the \(u_1u_2u_3\) coordinate system. This surface will be defined by the equation \(f(u_1,u_2,u_3)=C\), where \(C\) is a constant. We can then take the gradient of this function to find the direction of steepest increase.

To find the partial derivatives of \(f\) with respect to \(u_i\), we can use the chain rule. For example, \(\frac{\partial f}{\partial u_1}\) can be found by taking the dot product of the gradient of \(f\) with \(\mathbf{U}_u\).

\[
\frac{\partial f}{\partial u_1} = \nabla f \cdot \mathbf{U}_u
\]

Similarly, \(\frac{\partial f}{\partial u_2}\) and \(\frac{\partial f}{\partial u_3}\) can be found by taking the dot product with \(\mathbf{U}_v\) and \(\mathbf{U}_z\), respectively.

I hope this helps you find your scalar function and its partial derivatives. Let me know if you have any further questions.
 

Related to Gradient and scalar function question

1. What is a gradient function?

A gradient function is a mathematical function that represents the rate of change or slope of a given variable in a multi-dimensional space. It is often used in calculus and physics to calculate the direction and magnitude of a change in a function.

2. How is a gradient function calculated?

A gradient function is calculated using partial derivatives of a multi-variable function. The partial derivatives are computed for each variable and then combined to form a vector, which represents the direction and magnitude of the change in the function.

3. What is the relationship between a gradient function and a scalar function?

A gradient function is the vector representation of the rate of change in a scalar function. In other words, the gradient function is the derivative of a scalar function with respect to each variable, and the vector points in the direction of the steepest increase in the scalar function.

4. How is a gradient function used in real-world applications?

A gradient function is used in a variety of fields, such as physics, engineering, and economics. It can be used to calculate the direction and magnitude of forces, determine the optimal path for a given system, and predict changes in a system over time.

5. Can a scalar function have multiple gradient functions?

No, a scalar function can only have one gradient function. This is because the gradient function represents the direction and magnitude of the steepest increase in the scalar function, and there can only be one steepest increase at any given point.

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